1) Most companies have very limited skill in building advanced models. While many folks are trying to achieve 5% or better accuracy, MOST folks are trying to achieve ANY accuracy (since they have nothing)
2) Many problems are not very complicated and do not require a custom model. From the blog post, the "cloud" example only requires a small amount of changes to classify for a specific domain - to have to build an entirely new model for that, or train on MM of images seems like overkill
3) AutoML (often) is better than humans already[1]. So if you want to achieve that 5%, you MIGHT need to use a machine anyway.
There are a few different reasons:
1) Most companies have very limited skill in building advanced models. While many folks are trying to achieve 5% or better accuracy, MOST folks are trying to achieve ANY accuracy (since they have nothing)
2) Many problems are not very complicated and do not require a custom model. From the blog post, the "cloud" example only requires a small amount of changes to classify for a specific domain - to have to build an entirely new model for that, or train on MM of images seems like overkill
3) AutoML (often) is better than humans already[1]. So if you want to achieve that 5%, you MIGHT need to use a machine anyway.
[1] https://arxiv.org/abs/1712.00559